First, we compare BHiVAE with previous models with Z-differ Score [1]}, SAP Score [2]} and MIG Score [3]} and present the results in Table REF . It is clear that our model BHiVAE is at the top and that the MIG metric is better than other popular models.
The high value of the Z-diff score indicates that learned disentangled representation has less variance on the attributes of generated data as corresponding dimension changing, while SAP measures the degree of coupling between data factors and representations. Additionally MIG metric uses mutual information to measure the correlation between the data factor and learned disentangled representation, and our work is just modeled from the perspective of mutual information, which makes us performs best on the MIG score.
